RNA properties, such as function and stability, are intricately tied to their two-dimensional conformations. This has spurred the development of computational models for predicting the RNA secondary structures, leveraging dynamic programming or machine learning (ML) techniques. These structures are governed by specific rules; for example, only Watson-Crick and Wobble pairs are allowed, and sequences must not form sharp bends. Recent efforts introduced a systematic approach to post-process the predictions made by ML algorithms, aiming to modify them to respect the constraints. However, we still observe instances violating the requirements, significantly reducing biological relevance. To address this challenge, we present a novel post-processing framework for ML-based predictions on RNA secondary structures, inspired by the assignment problem in integer linear programming. Our algorithm offers a theoretical guarantee, ensuring that the resulting predictions adhere to the fundamental constraints of RNAs. Empirical evidence supports the efficacy of our approach, demonstrating improved predictive performance with no constraint violation, while requiring less running time.

In millimeter wave (mmWave) communication systems with hybrid multiple-input multiple-output (MIMO) processors, it is often necessary to employ analog beam sweeping during pilot signal transmission/reception to increase the signal to noise ratio (SNR). In this paper, we develop a process, called the space-time (ST) alignment, for receiving pilot sequences for mmWave channel estimation from the signal received during beam sweeping. The spatial alignment removes the effect of beam sweeping on pilot arrival time (PATs). The process for locating the temporal windows on the received signal to obtain pilot-containing sequences for channel estimation is called the temporal alignment. Simulation results demonstrate that mmWave channels can be estimated successfully using the sequences obtained via the ST alignment.